Efficient Causal Discovery for Robotics Applications
This addresses the need for fast and accurate causal discovery in robotics for environments like warehouses and hospitals, though it appears incremental as it builds on existing PCMCI methods.
The paper tackles the problem of enabling robots to understand cause-and-effect relationships in human-robot interactions for real-time applications, presenting F-PCMCI, which accurately and promptly reconstructs causal models to enhance interaction quality.
Using robots for automating tasks in environments shared with humans, such as warehouses, shopping centres, or hospitals, requires these robots to comprehend the fundamental physical interactions among nearby agents and objects. Specifically, creating models to represent cause-and-effect relationships among these elements can aid in predicting unforeseen human behaviours and anticipate the outcome of particular robot actions. To be suitable for robots, causal analysis must be both fast and accurate, meeting real-time demands and the limited computational resources typical in most robotics applications. In this paper, we present a practical demonstration of our approach for fast and accurate causal analysis, known as Filtered PCMCI (F-PCMCI), along with a real-world robotics application. The provided application illustrates how our F-PCMCI can accurately and promptly reconstruct the causal model of a human-robot interaction scenario, which can then be leveraged to enhance the quality of the interaction.